Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations438991
Missing cells1111698
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory103.8 MiB
Average record size in memory248.0 B

Variable types

Numeric19
Categorical7
Text5

Alerts

annual_inc is highly overall correlated with annual_inc_jointHigh correlation
annual_inc_joint is highly overall correlated with annual_inc and 1 other fieldsHigh correlation
application_type is highly overall correlated with annual_inc_joint and 1 other fieldsHigh correlation
bc_util is highly overall correlated with percent_bc_gt_75High correlation
delinq_2yrs is highly overall correlated with mths_since_last_delinqHigh correlation
dti is highly overall correlated with dti_jointHigh correlation
dti_joint is highly overall correlated with application_type and 1 other fieldsHigh correlation
funded_amnt is highly overall correlated with installmentHigh correlation
installment is highly overall correlated with funded_amntHigh correlation
mths_since_last_delinq is highly overall correlated with delinq_2yrsHigh correlation
open_acc is highly overall correlated with total_accHigh correlation
percent_bc_gt_75 is highly overall correlated with bc_utilHigh correlation
total_acc is highly overall correlated with open_acc and 1 other fieldsHigh correlation
total_bal_ex_mort is highly overall correlated with total_accHigh correlation
application_type is highly imbalanced (67.7%) Imbalance
emp_title has 30367 (6.9%) missing values Missing
emp_length has 30013 (6.8%) missing values Missing
mths_since_last_delinq has 213676 (48.7%) missing values Missing
annual_inc_joint has 413182 (94.1%) missing values Missing
dti_joint has 413183 (94.1%) missing values Missing
bc_util has 5389 (1.2%) missing values Missing
percent_bc_gt_75 has 5217 (1.2%) missing values Missing
annual_inc is highly skewed (γ1 = 47.44770867) Skewed
dti is highly skewed (γ1 = 30.87602985) Skewed
delinq_2yrs has 349495 (79.6%) zeros Zeros
inq_last_12m has 120380 (27.4%) zeros Zeros
acc_open_past_24mths has 18634 (4.2%) zeros Zeros
bc_util has 5303 (1.2%) zeros Zeros
mort_acc has 185192 (42.2%) zeros Zeros
num_accts_ever_120_pd has 333074 (75.9%) zeros Zeros
percent_bc_gt_75 has 125602 (28.6%) zeros Zeros

Reproduction

Analysis started2025-03-06 04:37:36.757293
Analysis finished2025-03-06 04:39:00.268342
Duration1 minute and 23.51 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

funded_amnt
Real number (ℝ)

High correlation 

Distinct1554
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14790.996
Minimum1000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:00.435983image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q17500
median12000
Q320000
95-th percentile35000
Maximum40000
Range39000
Interquartile range (IQR)12500

Descriptive statistics

Standard deviation9323.7409
Coefficient of variation (CV)0.630366
Kurtosis-0.11482476
Mean14790.996
Median Absolute Deviation (MAD)6000
Skewness0.80461744
Sum6.4931141 × 109
Variance86932145
MonotonicityNot monotonic
2025-03-05T23:39:00.551000image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 34908
 
8.0%
20000 24266
 
5.5%
12000 23752
 
5.4%
15000 22746
 
5.2%
5000 18151
 
4.1%
35000 16335
 
3.7%
8000 15960
 
3.6%
6000 15280
 
3.5%
16000 13852
 
3.2%
24000 12017
 
2.7%
Other values (1544) 241724
55.1%
ValueCountFrequency (%)
1000 2235
0.5%
1025 5
 
< 0.1%
1050 13
 
< 0.1%
1075 6
 
< 0.1%
1100 61
 
< 0.1%
1125 5
 
< 0.1%
1150 8
 
< 0.1%
1175 2
 
< 0.1%
1200 825
 
0.2%
1225 4
 
< 0.1%
ValueCountFrequency (%)
40000 6584
1.5%
39975 4
 
< 0.1%
39925 4
 
< 0.1%
39900 3
 
< 0.1%
39875 1
 
< 0.1%
39850 3
 
< 0.1%
39825 3
 
< 0.1%
39800 6
 
< 0.1%
39775 7
 
< 0.1%
39750 3
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
36 months
322236 
60 months
116755 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4389910
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 36 months
4th row 60 months
5th row 60 months

Common Values

ValueCountFrequency (%)
36 months 322236
73.4%
60 months 116755
 
26.6%

Length

2025-03-05T23:39:00.654513image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T23:39:00.726417image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
months 438991
50.0%
36 322236
36.7%
60 116755
 
13.3%

Most occurring characters

ValueCountFrequency (%)
877982
20.0%
6 438991
10.0%
t 438991
10.0%
m 438991
10.0%
o 438991
10.0%
n 438991
10.0%
s 438991
10.0%
h 438991
10.0%
3 322236
 
7.3%
0 116755
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4389910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
877982
20.0%
6 438991
10.0%
t 438991
10.0%
m 438991
10.0%
o 438991
10.0%
n 438991
10.0%
s 438991
10.0%
h 438991
10.0%
3 322236
 
7.3%
0 116755
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4389910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
877982
20.0%
6 438991
10.0%
t 438991
10.0%
m 438991
10.0%
o 438991
10.0%
n 438991
10.0%
s 438991
10.0%
h 438991
10.0%
3 322236
 
7.3%
0 116755
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4389910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
877982
20.0%
6 438991
10.0%
t 438991
10.0%
m 438991
10.0%
o 438991
10.0%
n 438991
10.0%
s 438991
10.0%
h 438991
10.0%
3 322236
 
7.3%
0 116755
 
2.7%
Distinct169
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:00.961355image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.7111649
Min length5

Characters and Unicode

Total characters2507150
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row11.99%
2nd row11.47%
3rd row16.29%
4th row12.99%
5th row15.31%
ValueCountFrequency (%)
5.32 17480
 
4.0%
11.99 17185
 
3.9%
11.49 14518
 
3.3%
13.99 11878
 
2.7%
13.49 11617
 
2.6%
16.02 10326
 
2.4%
12.74 10067
 
2.3%
12.62 9947
 
2.3%
10.42 9659
 
2.2%
9.44 9436
 
2.1%
Other values (159) 316878
72.2%
2025-03-05T23:39:01.267813image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 438991
17.5%
% 438991
17.5%
9 381894
15.2%
1 357153
14.2%
4 173735
 
6.9%
2 147194
 
5.9%
7 122216
 
4.9%
3 107268
 
4.3%
0 103548
 
4.1%
5 101907
 
4.1%
Other values (2) 134253
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2507150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 438991
17.5%
% 438991
17.5%
9 381894
15.2%
1 357153
14.2%
4 173735
 
6.9%
2 147194
 
5.9%
7 122216
 
4.9%
3 107268
 
4.3%
0 103548
 
4.1%
5 101907
 
4.1%
Other values (2) 134253
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2507150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 438991
17.5%
% 438991
17.5%
9 381894
15.2%
1 357153
14.2%
4 173735
 
6.9%
2 147194
 
5.9%
7 122216
 
4.9%
3 107268
 
4.3%
0 103548
 
4.1%
5 101907
 
4.1%
Other values (2) 134253
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2507150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 438991
17.5%
% 438991
17.5%
9 381894
15.2%
1 357153
14.2%
4 173735
 
6.9%
2 147194
 
5.9%
7 122216
 
4.9%
3 107268
 
4.3%
0 103548
 
4.1%
5 101907
 
4.1%
Other values (2) 134253
 
5.4%

installment
Real number (ℝ)

High correlation 

Distinct44023
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.45142
Minimum6.68
Maximum1719.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:01.361755image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum6.68
5-th percentile102.65
Q1241.13
median370.91
Q3593.49
95-th percentile1019.37
Maximum1719.83
Range1713.15
Interquartile range (IQR)352.36

Descriptive statistics

Standard deviation277.26239
Coefficient of variation (CV)0.62523735
Kurtosis0.73586158
Mean443.45142
Median Absolute Deviation (MAD)162.82
Skewness1.0458729
Sum1.9467118 × 108
Variance76874.434
MonotonicityNot monotonic
2025-03-05T23:39:01.482760image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301.15 1613
 
0.4%
332.1 1287
 
0.3%
329.72 1214
 
0.3%
602.3 1147
 
0.3%
361.38 1082
 
0.2%
451.73 1044
 
0.2%
240.92 873
 
0.2%
150.58 853
 
0.2%
166.05 852
 
0.2%
164.86 844
 
0.2%
Other values (44013) 428182
97.5%
ValueCountFrequency (%)
6.68 1
 
< 0.1%
30.12 29
< 0.1%
30.46 2
 
< 0.1%
30.65 8
 
< 0.1%
30.75 6
 
< 0.1%
30.86 1
 
< 0.1%
30.87 5
 
< 0.1%
30.88 19
< 0.1%
30.91 13
< 0.1%
30.98 23
< 0.1%
ValueCountFrequency (%)
1719.83 1
 
< 0.1%
1717.63 1
 
< 0.1%
1715.42 1
 
< 0.1%
1714.54 3
 
< 0.1%
1618.03 2
 
< 0.1%
1607.8 1
 
< 0.1%
1569.11 4
< 0.1%
1566.8 9
< 0.1%
1556.03 1
 
< 0.1%
1546.52 9
< 0.1%

emp_title
Text

Missing 

Distinct120032
Distinct (%)29.4%
Missing30367
Missing (%)6.9%
Memory size3.3 MiB
2025-03-05T23:39:01.769320image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length40
Median length31
Mean length15.195762
Min length1

Characters and Unicode

Total characters6209353
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94513 ?
Unique (%)23.1%

Sample

1st rowFraud analyst
2nd rowClient Relations Manager
3rd rowRegisterd nurse
4th rowFinancial Advisor
5th rowPharmacist-in-Charge
ValueCountFrequency (%)
manager 65163
 
8.1%
director 17463
 
2.2%
sales 15538
 
1.9%
assistant 15157
 
1.9%
supervisor 12412
 
1.5%
teacher 12226
 
1.5%
specialist 11399
 
1.4%
driver 11034
 
1.4%
senior 11004
 
1.4%
engineer 10872
 
1.4%
Other values (23099) 619103
77.3%
2025-03-05T23:39:02.092299image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 684050
 
11.0%
r 555782
 
9.0%
a 492441
 
7.9%
440993
 
7.1%
i 433596
 
7.0%
n 423240
 
6.8%
t 383457
 
6.2%
s 301015
 
4.8%
o 299302
 
4.8%
c 263851
 
4.2%
Other values (106) 1931626
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6209353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 684050
 
11.0%
r 555782
 
9.0%
a 492441
 
7.9%
440993
 
7.1%
i 433596
 
7.0%
n 423240
 
6.8%
t 383457
 
6.2%
s 301015
 
4.8%
o 299302
 
4.8%
c 263851
 
4.2%
Other values (106) 1931626
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6209353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 684050
 
11.0%
r 555782
 
9.0%
a 492441
 
7.9%
440993
 
7.1%
i 433596
 
7.0%
n 423240
 
6.8%
t 383457
 
6.2%
s 301015
 
4.8%
o 299302
 
4.8%
c 263851
 
4.2%
Other values (106) 1931626
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6209353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 684050
 
11.0%
r 555782
 
9.0%
a 492441
 
7.9%
440993
 
7.1%
i 433596
 
7.0%
n 423240
 
6.8%
t 383457
 
6.2%
s 301015
 
4.8%
o 299302
 
4.8%
c 263851
 
4.2%
Other values (106) 1931626
31.1%

emp_length
Categorical

Missing 

Distinct11
Distinct (%)< 0.1%
Missing30013
Missing (%)6.8%
Memory size3.3 MiB
10+ years
148471 
2 years
39794 
< 1 year
37836 
3 years
35018 
1 year
29239 
Other values (6)
118620 

Length

Max length9
Median length8
Mean length7.7470793
Min length6

Characters and Unicode

Total characters3168385
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8 years
2nd row< 1 year
3rd row8 years
4th row5 years
5th row3 years

Common Values

ValueCountFrequency (%)
10+ years 148471
33.8%
2 years 39794
 
9.1%
< 1 year 37836
 
8.6%
3 years 35018
 
8.0%
1 year 29239
 
6.7%
5 years 26483
 
6.0%
4 years 26208
 
6.0%
6 years 18920
 
4.3%
9 years 16068
 
3.7%
8 years 15892
 
3.6%
(Missing) 30013
 
6.8%

Length

2025-03-05T23:39:02.172299image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 341903
40.0%
10 148471
17.3%
1 67075
 
7.8%
year 67075
 
7.8%
2 39794
 
4.6%
37836
 
4.4%
3 35018
 
4.1%
5 26483
 
3.1%
4 26208
 
3.1%
6 18920
 
2.2%
Other values (3) 47009
 
5.5%

Most occurring characters

ValueCountFrequency (%)
446814
14.1%
y 408978
12.9%
r 408978
12.9%
a 408978
12.9%
e 408978
12.9%
s 341903
10.8%
1 215546
6.8%
0 148471
 
4.7%
+ 148471
 
4.7%
2 39794
 
1.3%
Other values (8) 191474
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3168385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
446814
14.1%
y 408978
12.9%
r 408978
12.9%
a 408978
12.9%
e 408978
12.9%
s 341903
10.8%
1 215546
6.8%
0 148471
 
4.7%
+ 148471
 
4.7%
2 39794
 
1.3%
Other values (8) 191474
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3168385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
446814
14.1%
y 408978
12.9%
r 408978
12.9%
a 408978
12.9%
e 408978
12.9%
s 341903
10.8%
1 215546
6.8%
0 148471
 
4.7%
+ 148471
 
4.7%
2 39794
 
1.3%
Other values (8) 191474
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3168385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
446814
14.1%
y 408978
12.9%
r 408978
12.9%
a 408978
12.9%
e 408978
12.9%
s 341903
10.8%
1 215546
6.8%
0 148471
 
4.7%
+ 148471
 
4.7%
2 39794
 
1.3%
Other values (8) 191474
6.0%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
MORTGAGE
214298 
RENT
171853 
OWN
52575 
ANY
 
262
NONE
 
3

Length

Max length8
Median length4
Mean length5.8322813
Min length3

Characters and Unicode

Total characters2560319
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
MORTGAGE 214298
48.8%
RENT 171853
39.1%
OWN 52575
 
12.0%
ANY 262
 
0.1%
NONE 3
 
< 0.1%

Length

2025-03-05T23:39:02.256341image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T23:39:02.318348image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 214298
48.8%
rent 171853
39.1%
own 52575
 
12.0%
any 262
 
0.1%
none 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 428596
16.7%
E 386154
15.1%
T 386151
15.1%
R 386151
15.1%
O 266876
10.4%
N 224696
8.8%
A 214560
8.4%
M 214298
8.4%
W 52575
 
2.1%
Y 262
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2560319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 428596
16.7%
E 386154
15.1%
T 386151
15.1%
R 386151
15.1%
O 266876
10.4%
N 224696
8.8%
A 214560
8.4%
M 214298
8.4%
W 52575
 
2.1%
Y 262
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2560319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 428596
16.7%
E 386154
15.1%
T 386151
15.1%
R 386151
15.1%
O 266876
10.4%
N 224696
8.8%
A 214560
8.4%
M 214298
8.4%
W 52575
 
2.1%
Y 262
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2560319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 428596
16.7%
E 386154
15.1%
T 386151
15.1%
R 386151
15.1%
O 266876
10.4%
N 224696
8.8%
A 214560
8.4%
M 214298
8.4%
W 52575
 
2.1%
Y 262
 
< 0.1%

annual_inc
Real number (ℝ)

High correlation  Skewed 

Distinct28079
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79786.051
Minimum0
Maximum10999200
Zeros273
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:02.415344image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28000
Q147594.9
median66264
Q395000
95-th percentile167000
Maximum10999200
Range10999200
Interquartile range (IQR)47405.1

Descriptive statistics

Standard deviation81942.723
Coefficient of variation (CV)1.0270307
Kurtosis4382.9964
Mean79786.051
Median Absolute Deviation (MAD)22736
Skewness47.447709
Sum3.5025358 × 1010
Variance6.7146099 × 109
MonotonicityNot monotonic
2025-03-05T23:39:02.602901image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 16919
 
3.9%
50000 14656
 
3.3%
65000 12633
 
2.9%
70000 12111
 
2.8%
80000 11773
 
2.7%
40000 11466
 
2.6%
75000 11373
 
2.6%
45000 10296
 
2.3%
55000 9782
 
2.2%
100000 9366
 
2.1%
Other values (28069) 318616
72.6%
ValueCountFrequency (%)
0 273
0.1%
1 8
 
< 0.1%
6 1
 
< 0.1%
10 3
 
< 0.1%
20 2
 
< 0.1%
25 1
 
< 0.1%
50 1
 
< 0.1%
100 3
 
< 0.1%
150 1
 
< 0.1%
200 2
 
< 0.1%
ValueCountFrequency (%)
10999200 1
< 0.1%
9550000 1
< 0.1%
9522972 1
< 0.1%
9225000 1
< 0.1%
8900000 1
< 0.1%
8500000 1
< 0.1%
8400000 1
< 0.1%
8300000 1
< 0.1%
8000000 1
< 0.1%
7500000 1
< 0.1%

loan_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Current
251552 
Fully Paid
132055 
Charged Off
40902 
Late
 
14482

Length

Max length11
Median length7
Mean length8.1761676
Min length4

Characters and Unicode

Total characters3589264
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCurrent
3rd rowCurrent
4th rowCurrent
5th rowCurrent

Common Values

ValueCountFrequency (%)
Current 251552
57.3%
Fully Paid 132055
30.1%
Charged Off 40902
 
9.3%
Late 14482
 
3.3%

Length

2025-03-05T23:39:02.760282image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T23:39:02.825271image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
current 251552
41.1%
fully 132055
21.6%
paid 132055
21.6%
charged 40902
 
6.7%
off 40902
 
6.7%
late 14482
 
2.4%

Most occurring characters

ValueCountFrequency (%)
r 544006
15.2%
u 383607
10.7%
e 306936
8.6%
C 292454
 
8.1%
t 266034
 
7.4%
l 264110
 
7.4%
n 251552
 
7.0%
a 187439
 
5.2%
172957
 
4.8%
d 172957
 
4.8%
Other values (9) 747212
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3589264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 544006
15.2%
u 383607
10.7%
e 306936
8.6%
C 292454
 
8.1%
t 266034
 
7.4%
l 264110
 
7.4%
n 251552
 
7.0%
a 187439
 
5.2%
172957
 
4.8%
d 172957
 
4.8%
Other values (9) 747212
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3589264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 544006
15.2%
u 383607
10.7%
e 306936
8.6%
C 292454
 
8.1%
t 266034
 
7.4%
l 264110
 
7.4%
n 251552
 
7.0%
a 187439
 
5.2%
172957
 
4.8%
d 172957
 
4.8%
Other values (9) 747212
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3589264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 544006
15.2%
u 383607
10.7%
e 306936
8.6%
C 292454
 
8.1%
t 266034
 
7.4%
l 264110
 
7.4%
n 251552
 
7.0%
a 187439
 
5.2%
172957
 
4.8%
d 172957
 
4.8%
Other values (9) 747212
20.8%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
debt_consolidation
246935 
credit_card
91374 
home_improvement
32995 
other
30875 
major_purchase
 
10685
Other values (9)
26127 

Length

Max length18
Median length18
Mean length14.753592
Min length3

Characters and Unicode

Total characters6476694
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowdebt_consolidation
2nd rowdebt_consolidation
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowdebt_consolidation

Common Values

ValueCountFrequency (%)
debt_consolidation 246935
56.3%
credit_card 91374
 
20.8%
home_improvement 32995
 
7.5%
other 30875
 
7.0%
major_purchase 10685
 
2.4%
medical 6248
 
1.4%
car 5062
 
1.2%
small_business 4930
 
1.1%
vacation 3585
 
0.8%
moving 3480
 
0.8%
Other values (4) 2822
 
0.6%

Length

2025-03-05T23:39:02.910270image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 246935
56.3%
credit_card 91374
 
20.8%
home_improvement 32995
 
7.5%
other 30875
 
7.0%
major_purchase 10685
 
2.4%
medical 6248
 
1.4%
car 5062
 
1.2%
small_business 4930
 
1.1%
vacation 3585
 
0.8%
moving 3480
 
0.8%
Other values (4) 2822
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 857946
13.2%
d 682869
10.5%
t 652700
10.1%
i 636484
9.8%
n 539452
8.3%
e 494034
7.6%
c 455264
7.0%
_ 387214
 
6.0%
a 383386
 
5.9%
s 279865
 
4.3%
Other values (12) 1107480
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6476694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 857946
13.2%
d 682869
10.5%
t 652700
10.1%
i 636484
9.8%
n 539452
8.3%
e 494034
7.6%
c 455264
7.0%
_ 387214
 
6.0%
a 383386
 
5.9%
s 279865
 
4.3%
Other values (12) 1107480
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6476694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 857946
13.2%
d 682869
10.5%
t 652700
10.1%
i 636484
9.8%
n 539452
8.3%
e 494034
7.6%
c 455264
7.0%
_ 387214
 
6.0%
a 383386
 
5.9%
s 279865
 
4.3%
Other values (12) 1107480
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6476694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 857946
13.2%
d 682869
10.5%
t 652700
10.1%
i 636484
9.8%
n 539452
8.3%
e 494034
7.6%
c 455264
7.0%
_ 387214
 
6.0%
a 383386
 
5.9%
s 279865
 
4.3%
Other values (12) 1107480
17.1%
Distinct908
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:03.157328image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2194955
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st row853xx
2nd row972xx
3rd row349xx
4th row601xx
5th row373xx
ValueCountFrequency (%)
750xx 4620
 
1.1%
112xx 4607
 
1.0%
945xx 4524
 
1.0%
606xx 4104
 
0.9%
300xx 3979
 
0.9%
331xx 3688
 
0.8%
070xx 3562
 
0.8%
770xx 3522
 
0.8%
330xx 3364
 
0.8%
891xx 3330
 
0.8%
Other values (898) 399691
91.0%
2025-03-05T23:39:03.466316image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 877982
40.0%
0 194324
 
8.9%
1 154910
 
7.1%
3 149372
 
6.8%
2 136088
 
6.2%
7 131806
 
6.0%
9 124822
 
5.7%
4 114617
 
5.2%
8 108268
 
4.9%
5 105540
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2194955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
x 877982
40.0%
0 194324
 
8.9%
1 154910
 
7.1%
3 149372
 
6.8%
2 136088
 
6.2%
7 131806
 
6.0%
9 124822
 
5.7%
4 114617
 
5.2%
8 108268
 
4.9%
5 105540
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2194955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
x 877982
40.0%
0 194324
 
8.9%
1 154910
 
7.1%
3 149372
 
6.8%
2 136088
 
6.2%
7 131806
 
6.0%
9 124822
 
5.7%
4 114617
 
5.2%
8 108268
 
4.9%
5 105540
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2194955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
x 877982
40.0%
0 194324
 
8.9%
1 154910
 
7.1%
3 149372
 
6.8%
2 136088
 
6.2%
7 131806
 
6.0%
9 124822
 
5.7%
4 114617
 
5.2%
8 108268
 
4.9%
5 105540
 
4.8%

addr_state
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
CA
58548 
TX
37043 
NY
36303 
FL
31858 
IL
 
18131
Other values (45)
257108 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters877982
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowOR
3rd rowFL
4th rowIL
5th rowTN

Common Values

ValueCountFrequency (%)
CA 58548
 
13.3%
TX 37043
 
8.4%
NY 36303
 
8.3%
FL 31858
 
7.3%
IL 18131
 
4.1%
NJ 16203
 
3.7%
OH 14878
 
3.4%
PA 14634
 
3.3%
GA 14448
 
3.3%
NC 12257
 
2.8%
Other values (40) 184688
42.1%

Length

2025-03-05T23:39:03.556271image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 58548
 
13.3%
tx 37043
 
8.4%
ny 36303
 
8.3%
fl 31858
 
7.3%
il 18131
 
4.1%
nj 16203
 
3.7%
oh 14878
 
3.4%
pa 14634
 
3.3%
ga 14448
 
3.3%
nc 12257
 
2.8%
Other values (40) 184688
42.1%

Most occurring characters

ValueCountFrequency (%)
A 143155
16.3%
N 101348
11.5%
C 93600
10.7%
L 60152
 
6.9%
T 56383
 
6.4%
M 54541
 
6.2%
I 48529
 
5.5%
Y 41501
 
4.7%
O 40134
 
4.6%
X 37043
 
4.2%
Other values (14) 201596
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 877982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 143155
16.3%
N 101348
11.5%
C 93600
10.7%
L 60152
 
6.9%
T 56383
 
6.4%
M 54541
 
6.2%
I 48529
 
5.5%
Y 41501
 
4.7%
O 40134
 
4.6%
X 37043
 
4.2%
Other values (14) 201596
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 877982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 143155
16.3%
N 101348
11.5%
C 93600
10.7%
L 60152
 
6.9%
T 56383
 
6.4%
M 54541
 
6.2%
I 48529
 
5.5%
Y 41501
 
4.7%
O 40134
 
4.6%
X 37043
 
4.2%
Other values (14) 201596
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 877982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 143155
16.3%
N 101348
11.5%
C 93600
10.7%
L 60152
 
6.9%
T 56383
 
6.4%
M 54541
 
6.2%
I 48529
 
5.5%
Y 41501
 
4.7%
O 40134
 
4.6%
X 37043
 
4.2%
Other values (14) 201596
23.0%

dti
Real number (ℝ)

High correlation  Skewed 

Distinct6986
Distinct (%)1.6%
Missing285
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean19.053124
Minimum-1
Maximum999
Zeros218
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size3.3 MiB
2025-03-05T23:39:03.652277image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5.12
Q112.13
median18.1
Q324.8
95-th percentile33.7
Maximum999
Range1000
Interquartile range (IQR)12.67

Descriptive statistics

Standard deviation14.427149
Coefficient of variation (CV)0.75720649
Kurtosis1875.6497
Mean19.053124
Median Absolute Deviation (MAD)6.3
Skewness30.87603
Sum8358719.6
Variance208.14262
MonotonicityNot monotonic
2025-03-05T23:39:03.780156image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.4 306
 
0.1%
20.4 300
 
0.1%
13.2 296
 
0.1%
18 294
 
0.1%
16.8 293
 
0.1%
21.6 290
 
0.1%
12 288
 
0.1%
19.2 287
 
0.1%
15.6 275
 
0.1%
10.8 274
 
0.1%
Other values (6976) 435803
99.3%
(Missing) 285
 
0.1%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 218
< 0.1%
0.01 3
 
< 0.1%
0.02 6
 
< 0.1%
0.03 4
 
< 0.1%
0.05 6
 
< 0.1%
0.06 6
 
< 0.1%
0.07 6
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
999 31
< 0.1%
995.17 1
 
< 0.1%
991.57 1
 
< 0.1%
886.77 1
 
< 0.1%
847.22 1
 
< 0.1%
831.97 1
 
< 0.1%
818.1 1
 
< 0.1%
775.9 1
 
< 0.1%
770 1
 
< 0.1%
762.5 1
 
< 0.1%

delinq_2yrs
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34341479
Minimum0
Maximum42
Zeros349495
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:03.890604image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93006333
Coefficient of variation (CV)2.7082798
Kurtosis65.440673
Mean0.34341479
Median Absolute Deviation (MAD)0
Skewness5.7037747
Sum150756
Variance0.86501779
MonotonicityNot monotonic
2025-03-05T23:39:03.978772image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 349495
79.6%
1 58564
 
13.3%
2 17683
 
4.0%
3 6562
 
1.5%
4 2905
 
0.7%
5 1511
 
0.3%
6 882
 
0.2%
7 482
 
0.1%
8 286
 
0.1%
9 193
 
< 0.1%
Other values (19) 428
 
0.1%
ValueCountFrequency (%)
0 349495
79.6%
1 58564
 
13.3%
2 17683
 
4.0%
3 6562
 
1.5%
4 2905
 
0.7%
5 1511
 
0.3%
6 882
 
0.2%
7 482
 
0.1%
8 286
 
0.1%
9 193
 
< 0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
36 1
 
< 0.1%
30 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
21 2
< 0.1%
20 4
< 0.1%
19 4
< 0.1%
Distinct680
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:04.213800image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2633946
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)< 0.1%

Sample

1st rowJul-12
2nd rowMar-04
3rd rowMay-05
4th rowAug-88
5th rowAug-98
ValueCountFrequency (%)
sep-05 3353
 
0.8%
sep-04 3351
 
0.8%
aug-04 3164
 
0.7%
aug-03 3158
 
0.7%
aug-05 3138
 
0.7%
sep-03 3132
 
0.7%
aug-06 3040
 
0.7%
oct-03 2981
 
0.7%
oct-04 2902
 
0.7%
aug-02 2852
 
0.6%
Other values (670) 407920
92.9%
2025-03-05T23:39:04.520769image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 438991
 
16.7%
0 274802
 
10.4%
9 163887
 
6.2%
u 114897
 
4.4%
e 109239
 
4.1%
a 101617
 
3.9%
J 101187
 
3.8%
A 78685
 
3.0%
p 77336
 
2.9%
c 75618
 
2.9%
Other values (23) 1097687
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2633946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 438991
 
16.7%
0 274802
 
10.4%
9 163887
 
6.2%
u 114897
 
4.4%
e 109239
 
4.1%
a 101617
 
3.9%
J 101187
 
3.8%
A 78685
 
3.0%
p 77336
 
2.9%
c 75618
 
2.9%
Other values (23) 1097687
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2633946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 438991
 
16.7%
0 274802
 
10.4%
9 163887
 
6.2%
u 114897
 
4.4%
e 109239
 
4.1%
a 101617
 
3.9%
J 101187
 
3.8%
A 78685
 
3.0%
p 77336
 
2.9%
c 75618
 
2.9%
Other values (23) 1097687
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2633946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 438991
 
16.7%
0 274802
 
10.4%
9 163887
 
6.2%
u 114897
 
4.4%
e 109239
 
4.1%
a 101617
 
3.9%
J 101187
 
3.8%
A 78685
 
3.0%
p 77336
 
2.9%
c 75618
 
2.9%
Other values (23) 1097687
41.7%

fico_range_high
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.84428
Minimum664
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:04.601759image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum664
5-th percentile664
Q1674
median694
Q3719
95-th percentile769
Maximum850
Range186
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.929589
Coefficient of variation (CV)0.046918655
Kurtosis1.43099
Mean701.84428
Median Absolute Deviation (MAD)20
Skewness1.2256408
Sum3.0810332 × 108
Variance1084.3579
MonotonicityNot monotonic
2025-03-05T23:39:04.710300image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
664 38799
 
8.8%
669 36735
 
8.4%
674 36052
 
8.2%
684 32415
 
7.4%
679 32368
 
7.4%
689 28302
 
6.4%
694 27848
 
6.3%
699 24932
 
5.7%
704 23840
 
5.4%
709 21494
 
4.9%
Other values (28) 136206
31.0%
ValueCountFrequency (%)
664 38799
8.8%
669 36735
8.4%
674 36052
8.2%
679 32368
7.4%
684 32415
7.4%
689 28302
6.4%
694 27848
6.3%
699 24932
5.7%
704 23840
5.4%
709 21494
4.9%
ValueCountFrequency (%)
850 88
 
< 0.1%
844 112
 
< 0.1%
839 180
 
< 0.1%
834 294
 
0.1%
829 439
 
0.1%
824 544
 
0.1%
819 765
0.2%
814 897
0.2%
809 1263
0.3%
804 1521
0.3%

mths_since_last_delinq
Real number (ℝ)

High correlation  Missing 

Distinct147
Distinct (%)0.1%
Missing213676
Missing (%)48.7%
Infinite0
Infinite (%)0.0%
Mean33.849042
Minimum0
Maximum195
Zeros460
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:05.249331image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q115
median30
Q349
95-th percentile74
Maximum195
Range195
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.887352
Coefficient of variation (CV)0.64661659
Kurtosis-0.64835153
Mean33.849042
Median Absolute Deviation (MAD)16
Skewness0.4945745
Sum7626697
Variance479.05619
MonotonicityNot monotonic
2025-03-05T23:39:05.373359image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 4364
 
1.0%
7 4347
 
1.0%
6 4338
 
1.0%
13 4335
 
1.0%
9 4295
 
1.0%
8 4098
 
0.9%
11 4062
 
0.9%
14 4053
 
0.9%
10 4042
 
0.9%
15 3987
 
0.9%
Other values (137) 183394
41.8%
(Missing) 213676
48.7%
ValueCountFrequency (%)
0 460
 
0.1%
1 1744
0.4%
2 2450
0.6%
3 3032
0.7%
4 3469
0.8%
5 3804
0.9%
6 4338
1.0%
7 4347
1.0%
8 4098
0.9%
9 4295
1.0%
ValueCountFrequency (%)
195 1
< 0.1%
192 1
< 0.1%
188 1
< 0.1%
178 1
< 0.1%
168 1
< 0.1%
162 1
< 0.1%
161 1
< 0.1%
150 1
< 0.1%
146 2
< 0.1%
142 1
< 0.1%

open_acc
Real number (ℝ)

High correlation 

Distinct74
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.746391
Minimum0
Maximum97
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:05.496573image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q315
95-th percentile23
Maximum97
Range97
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.7677068
Coefficient of variation (CV)0.49101947
Kurtosis3.6229375
Mean11.746391
Median Absolute Deviation (MAD)3
Skewness1.3414156
Sum5156560
Variance33.266442
MonotonicityNot monotonic
2025-03-05T23:39:05.618527image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 37009
 
8.4%
10 36088
 
8.2%
8 36028
 
8.2%
11 33748
 
7.7%
7 33619
 
7.7%
12 30503
 
6.9%
6 27891
 
6.4%
13 26563
 
6.1%
14 22872
 
5.2%
5 20893
 
4.8%
Other values (64) 133777
30.5%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 257
 
0.1%
2 1859
 
0.4%
3 6306
 
1.4%
4 13063
 
3.0%
5 20893
4.8%
6 27891
6.4%
7 33619
7.7%
8 36028
8.2%
9 37009
8.4%
ValueCountFrequency (%)
97 1
 
< 0.1%
93 1
 
< 0.1%
86 2
< 0.1%
81 1
 
< 0.1%
80 2
< 0.1%
75 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
66 1
 
< 0.1%
65 3
< 0.1%

revol_bal
Real number (ℝ)

Distinct61561
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16638.971
Minimum0
Maximum2559552
Zeros2153
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:05.759620image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1568
Q15815
median11040
Q319850
95-th percentile45963
Maximum2559552
Range2559552
Interquartile range (IQR)14035

Descriptive statistics

Standard deviation23834.503
Coefficient of variation (CV)1.4324505
Kurtosis550.57395
Mean16638.971
Median Absolute Deviation (MAD)6231
Skewness12.557355
Sum7.3043585 × 109
Variance5.6808352 × 108
MonotonicityNot monotonic
2025-03-05T23:39:05.878566image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2153
 
0.5%
8 43
 
< 0.1%
10 43
 
< 0.1%
5738 43
 
< 0.1%
4769 40
 
< 0.1%
5039 40
 
< 0.1%
5092 40
 
< 0.1%
5721 39
 
< 0.1%
8170 39
 
< 0.1%
5396 39
 
< 0.1%
Other values (61551) 436472
99.4%
ValueCountFrequency (%)
0 2153
0.5%
1 22
 
< 0.1%
2 30
 
< 0.1%
3 29
 
< 0.1%
4 31
 
< 0.1%
5 24
 
< 0.1%
6 27
 
< 0.1%
7 22
 
< 0.1%
8 43
 
< 0.1%
9 28
 
< 0.1%
ValueCountFrequency (%)
2559552 1
< 0.1%
1698749 1
< 0.1%
1696796 1
< 0.1%
1039013 1
< 0.1%
1023940 1
< 0.1%
971736 1
< 0.1%
895286 1
< 0.1%
893598 1
< 0.1%
853207 1
< 0.1%
832764 1
< 0.1%
Distinct1163
Distinct (%)0.3%
Missing359
Missing (%)0.1%
Memory size3.3 MiB
2025-03-05T23:39:06.148574image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5027563
Min length2

Characters and Unicode

Total characters2413685
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)< 0.1%

Sample

1st row6.90%
2nd row90.10%
3rd row49.10%
4th row78.10%
5th row96%
ValueCountFrequency (%)
0 2235
 
0.5%
55 933
 
0.2%
56 932
 
0.2%
60 906
 
0.2%
48 897
 
0.2%
57 891
 
0.2%
51 889
 
0.2%
42 867
 
0.2%
45 867
 
0.2%
54 866
 
0.2%
Other values (1153) 428349
97.7%
2025-03-05T23:39:06.487631image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 438632
18.2%
0 421223
17.5%
. 373181
15.5%
4 145327
 
6.0%
5 144619
 
6.0%
3 144591
 
6.0%
6 137810
 
5.7%
2 134388
 
5.6%
7 128993
 
5.3%
1 121547
 
5.0%
Other values (2) 223374
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2413685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 438632
18.2%
0 421223
17.5%
. 373181
15.5%
4 145327
 
6.0%
5 144619
 
6.0%
3 144591
 
6.0%
6 137810
 
5.7%
2 134388
 
5.6%
7 128993
 
5.3%
1 121547
 
5.0%
Other values (2) 223374
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2413685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 438632
18.2%
0 421223
17.5%
. 373181
15.5%
4 145327
 
6.0%
5 144619
 
6.0%
3 144591
 
6.0%
6 137810
 
5.7%
2 134388
 
5.6%
7 128993
 
5.3%
1 121547
 
5.0%
Other values (2) 223374
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2413685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 438632
18.2%
0 421223
17.5%
. 373181
15.5%
4 145327
 
6.0%
5 144619
 
6.0%
3 144591
 
6.0%
6 137810
 
5.7%
2 134388
 
5.6%
7 128993
 
5.3%
1 121547
 
5.0%
Other values (2) 223374
9.3%

total_acc
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.908381
Minimum2
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:06.575623image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q115
median22
Q330
95-th percentile46
Maximum173
Range171
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.938711
Coefficient of variation (CV)0.49935254
Kurtosis2.1537577
Mean23.908381
Median Absolute Deviation (MAD)7
Skewness1.066801
Sum10495564
Variance142.53281
MonotonicityNot monotonic
2025-03-05T23:39:06.699592image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 16176
 
3.7%
19 16169
 
3.7%
18 16156
 
3.7%
21 16135
 
3.7%
16 16011
 
3.6%
17 16007
 
3.6%
22 15363
 
3.5%
15 15326
 
3.5%
23 15236
 
3.5%
14 14831
 
3.4%
Other values (116) 281581
64.1%
ValueCountFrequency (%)
2 238
 
0.1%
3 848
 
0.2%
4 1907
 
0.4%
5 3174
 
0.7%
6 4741
 
1.1%
7 6332
1.4%
8 7633
1.7%
9 9294
2.1%
10 10746
2.4%
11 12045
2.7%
ValueCountFrequency (%)
173 1
 
< 0.1%
165 1
 
< 0.1%
160 1
 
< 0.1%
151 1
 
< 0.1%
144 2
< 0.1%
137 2
< 0.1%
136 1
 
< 0.1%
122 3
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%

application_type
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Individual
413182 
Joint App
 
25809

Length

Max length10
Median length10
Mean length9.9412084
Min length9

Characters and Unicode

Total characters4364101
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 413182
94.1%
Joint App 25809
 
5.9%

Length

2025-03-05T23:39:06.831615image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T23:39:06.888669image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
individual 413182
88.9%
joint 25809
 
5.6%
app 25809
 
5.6%

Most occurring characters

ValueCountFrequency (%)
i 852173
19.5%
d 826364
18.9%
n 438991
10.1%
I 413182
9.5%
v 413182
9.5%
u 413182
9.5%
a 413182
9.5%
l 413182
9.5%
p 51618
 
1.2%
J 25809
 
0.6%
Other values (4) 103236
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4364101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 852173
19.5%
d 826364
18.9%
n 438991
10.1%
I 413182
9.5%
v 413182
9.5%
u 413182
9.5%
a 413182
9.5%
l 413182
9.5%
p 51618
 
1.2%
J 25809
 
0.6%
Other values (4) 103236
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4364101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 852173
19.5%
d 826364
18.9%
n 438991
10.1%
I 413182
9.5%
v 413182
9.5%
u 413182
9.5%
a 413182
9.5%
l 413182
9.5%
p 51618
 
1.2%
J 25809
 
0.6%
Other values (4) 103236
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4364101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 852173
19.5%
d 826364
18.9%
n 438991
10.1%
I 413182
9.5%
v 413182
9.5%
u 413182
9.5%
a 413182
9.5%
l 413182
9.5%
p 51618
 
1.2%
J 25809
 
0.6%
Other values (4) 103236
 
2.4%

annual_inc_joint
Real number (ℝ)

High correlation  Missing 

Distinct5103
Distinct (%)19.8%
Missing413182
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean117801.75
Minimum13470
Maximum1300000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:06.967742image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum13470
5-th percentile52000
Q181000
median107000
Q3140000
95-th percentile215000
Maximum1300000
Range1286530
Interquartile range (IQR)59000

Descriptive statistics

Standard deviation57901.008
Coefficient of variation (CV)0.49151231
Kurtosis27.134676
Mean117801.75
Median Absolute Deviation (MAD)29000
Skewness3.1057193
Sum3.0403453 × 109
Variance3.3525267 × 109
MonotonicityNot monotonic
2025-03-05T23:39:07.090676image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 488
 
0.1%
120000 437
 
0.1%
110000 432
 
0.1%
90000 414
 
0.1%
130000 392
 
0.1%
80000 356
 
0.1%
95000 324
 
0.1%
105000 322
 
0.1%
150000 315
 
0.1%
140000 309
 
0.1%
Other values (5093) 22020
 
5.0%
(Missing) 413182
94.1%
ValueCountFrequency (%)
13470 1
< 0.1%
15801 1
< 0.1%
17200 1
< 0.1%
18000 2
< 0.1%
18072 1
< 0.1%
18180 1
< 0.1%
19000 1
< 0.1%
19200 1
< 0.1%
19464 1
< 0.1%
19528 1
< 0.1%
ValueCountFrequency (%)
1300000 1
< 0.1%
1050000 2
< 0.1%
925000 1
< 0.1%
900000 1
< 0.1%
850000 1
< 0.1%
800000 1
< 0.1%
780000 1
< 0.1%
760000 1
< 0.1%
750000 1
< 0.1%
744000 1
< 0.1%

dti_joint
Real number (ℝ)

High correlation  Missing 

Distinct3548
Distinct (%)13.7%
Missing413183
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean19.06079
Minimum0
Maximum69.49
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:07.224716image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.34
Q113.63
median18.71
Q324.24
95-th percentile31.93
Maximum69.49
Range69.49
Interquartile range (IQR)10.61

Descriptive statistics

Standard deviation7.45873
Coefficient of variation (CV)0.39131273
Kurtosis-0.22952902
Mean19.06079
Median Absolute Deviation (MAD)5.3
Skewness0.22121524
Sum491920.88
Variance55.632653
MonotonicityNot monotonic
2025-03-05T23:39:07.343713image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.02 23
 
< 0.1%
14.43 22
 
< 0.1%
18.1 22
 
< 0.1%
19.49 22
 
< 0.1%
14.85 21
 
< 0.1%
14.84 21
 
< 0.1%
17.73 21
 
< 0.1%
12.16 21
 
< 0.1%
22.09 21
 
< 0.1%
14.1 21
 
< 0.1%
Other values (3538) 25593
 
5.8%
(Missing) 413183
94.1%
ValueCountFrequency (%)
0 5
< 0.1%
0.21 1
 
< 0.1%
0.23 1
 
< 0.1%
0.27 1
 
< 0.1%
0.29 1
 
< 0.1%
0.32 2
 
< 0.1%
0.36 1
 
< 0.1%
0.42 1
 
< 0.1%
0.43 1
 
< 0.1%
0.44 2
 
< 0.1%
ValueCountFrequency (%)
69.49 1
< 0.1%
61.9 1
< 0.1%
55.52 1
< 0.1%
54.19 1
< 0.1%
50.75 1
< 0.1%
45.39 1
< 0.1%
45.34 1
< 0.1%
45.33 1
< 0.1%
43.97 1
< 0.1%
43.91 1
< 0.1%

inq_last_12m
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)< 0.1%
Missing27
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.088128
Minimum0
Maximum42
Zeros120380
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:07.451685image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum42
Range42
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.387063
Coefficient of variation (CV)1.1431593
Kurtosis10.061928
Mean2.088128
Median Absolute Deviation (MAD)1
Skewness2.3238036
Sum916613
Variance5.6980697
MonotonicityNot monotonic
2025-03-05T23:39:07.547673image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 120380
27.4%
1 106124
24.2%
2 76005
17.3%
3 49958
11.4%
4 31646
 
7.2%
5 19577
 
4.5%
6 12306
 
2.8%
7 7736
 
1.8%
8 5077
 
1.2%
9 3249
 
0.7%
Other values (29) 6906
 
1.6%
ValueCountFrequency (%)
0 120380
27.4%
1 106124
24.2%
2 76005
17.3%
3 49958
11.4%
4 31646
 
7.2%
5 19577
 
4.5%
6 12306
 
2.8%
7 7736
 
1.8%
8 5077
 
1.2%
9 3249
 
0.7%
ValueCountFrequency (%)
42 1
 
< 0.1%
41 1
 
< 0.1%
38 1
 
< 0.1%
37 1
 
< 0.1%
36 2
 
< 0.1%
34 3
 
< 0.1%
32 5
< 0.1%
31 4
< 0.1%
30 7
< 0.1%
29 9
< 0.1%

acc_open_past_24mths
Real number (ℝ)

Zeros 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6610067
Minimum0
Maximum61
Zeros18634
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:07.644673image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum61
Range61
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2614662
Coefficient of variation (CV)0.69973429
Kurtosis4.7203075
Mean4.6610067
Median Absolute Deviation (MAD)2
Skewness1.44407
Sum2046140
Variance10.637162
MonotonicityNot monotonic
2025-03-05T23:39:07.768675image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
3 64657
14.7%
4 60320
13.7%
2 58605
13.3%
5 51404
11.7%
1 42365
9.7%
6 40495
9.2%
7 30549
7.0%
8 22139
 
5.0%
0 18634
 
4.2%
9 15768
 
3.6%
Other values (37) 34055
7.8%
ValueCountFrequency (%)
0 18634
 
4.2%
1 42365
9.7%
2 58605
13.3%
3 64657
14.7%
4 60320
13.7%
5 51404
11.7%
6 40495
9.2%
7 30549
7.0%
8 22139
 
5.0%
9 15768
 
3.6%
ValueCountFrequency (%)
61 1
 
< 0.1%
56 1
 
< 0.1%
46 2
< 0.1%
45 4
< 0.1%
44 1
 
< 0.1%
43 2
< 0.1%
40 4
< 0.1%
39 2
< 0.1%
38 2
< 0.1%
37 4
< 0.1%

bc_util
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1269
Distinct (%)0.3%
Missing5389
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean56.734858
Minimum0
Maximum252.3
Zeros5303
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:07.883716image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.5
Q134.1
median58.3
Q381.9
95-th percentile97.6
Maximum252.3
Range252.3
Interquartile range (IQR)47.8

Descriptive statistics

Standard deviation28.526861
Coefficient of variation (CV)0.50281011
Kurtosis-1.0342881
Mean56.734858
Median Absolute Deviation (MAD)23.8
Skewness-0.21031462
Sum24600348
Variance813.78177
MonotonicityNot monotonic
2025-03-05T23:39:08.007673image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5303
 
1.2%
99 1318
 
0.3%
98 1312
 
0.3%
96 1215
 
0.3%
97 1170
 
0.3%
95 1140
 
0.3%
94 1048
 
0.2%
93 1043
 
0.2%
92 974
 
0.2%
90 936
 
0.2%
Other values (1259) 418143
95.3%
(Missing) 5389
 
1.2%
ValueCountFrequency (%)
0 5303
1.2%
0.1 480
 
0.1%
0.2 382
 
0.1%
0.3 357
 
0.1%
0.4 267
 
0.1%
0.5 291
 
0.1%
0.6 250
 
0.1%
0.7 266
 
0.1%
0.8 259
 
0.1%
0.9 234
 
0.1%
ValueCountFrequency (%)
252.3 1
< 0.1%
195.6 1
< 0.1%
190.3 1
< 0.1%
189.9 1
< 0.1%
189.8 1
< 0.1%
187 1
< 0.1%
184.4 1
< 0.1%
182.9 1
< 0.1%
182.3 1
< 0.1%
166.4 1
< 0.1%

mort_acc
Real number (ℝ)

Zeros 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4862423
Minimum0
Maximum94
Zeros185192
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:08.119695image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum94
Range94
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8096674
Coefficient of variation (CV)1.2176126
Kurtosis20.925614
Mean1.4862423
Median Absolute Deviation (MAD)1
Skewness1.91959
Sum652447
Variance3.2748961
MonotonicityNot monotonic
2025-03-05T23:39:08.227259image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 185192
42.2%
1 81652
18.6%
2 66719
 
15.2%
3 45877
 
10.5%
4 28610
 
6.5%
5 15663
 
3.6%
6 7937
 
1.8%
7 3748
 
0.9%
8 1751
 
0.4%
9 866
 
0.2%
Other values (20) 976
 
0.2%
ValueCountFrequency (%)
0 185192
42.2%
1 81652
18.6%
2 66719
 
15.2%
3 45877
 
10.5%
4 28610
 
6.5%
5 15663
 
3.6%
6 7937
 
1.8%
7 3748
 
0.9%
8 1751
 
0.4%
9 866
 
0.2%
ValueCountFrequency (%)
94 1
< 0.1%
51 1
< 0.1%
37 1
< 0.1%
31 1
< 0.1%
30 1
< 0.1%
28 1
< 0.1%
27 1
< 0.1%
23 2
< 0.1%
22 1
< 0.1%
20 2
< 0.1%

num_accts_ever_120_pd
Real number (ℝ)

Zeros 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53734587
Minimum0
Maximum51
Zeros333074
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:08.327375image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum51
Range51
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4285503
Coefficient of variation (CV)2.6585304
Kurtosis53.240257
Mean0.53734587
Median Absolute Deviation (MAD)0
Skewness5.4581544
Sum235890
Variance2.040756
MonotonicityNot monotonic
2025-03-05T23:39:08.437346image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 333074
75.9%
1 56389
 
12.8%
2 22053
 
5.0%
3 10387
 
2.4%
4 6077
 
1.4%
5 3590
 
0.8%
6 2481
 
0.6%
7 1544
 
0.4%
8 1024
 
0.2%
9 648
 
0.1%
Other values (30) 1724
 
0.4%
ValueCountFrequency (%)
0 333074
75.9%
1 56389
 
12.8%
2 22053
 
5.0%
3 10387
 
2.4%
4 6077
 
1.4%
5 3590
 
0.8%
6 2481
 
0.6%
7 1544
 
0.4%
8 1024
 
0.2%
9 648
 
0.1%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
42 1
 
< 0.1%
36 2
< 0.1%
35 2
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 2
< 0.1%
30 3
< 0.1%

percent_bc_gt_75
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct207
Distinct (%)< 0.1%
Missing5217
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean40.881013
Minimum0
Maximum100
Zeros125602
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:08.543412image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33.3
Q366.7
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)66.7

Descriptive statistics

Standard deviation36.189174
Coefficient of variation (CV)0.88523182
Kurtosis-1.2066557
Mean40.881013
Median Absolute Deviation (MAD)33.3
Skewness0.38163727
Sum17733121
Variance1309.6563
MonotonicityNot monotonic
2025-03-05T23:39:08.660391image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125602
28.6%
100 71936
16.4%
50 45562
 
10.4%
33.3 29777
 
6.8%
66.7 25324
 
5.8%
25 19855
 
4.5%
75 13614
 
3.1%
20 13381
 
3.0%
40 10827
 
2.5%
16.7 8762
 
2.0%
Other values (197) 69134
15.7%
ValueCountFrequency (%)
0 125602
28.6%
1.4 1
 
< 0.1%
1.9 1
 
< 0.1%
2.2 1
 
< 0.1%
2.3 1
 
< 0.1%
2.4 1
 
< 0.1%
2.7 1
 
< 0.1%
3 1
 
< 0.1%
3.2 1
 
< 0.1%
3.3 1
 
< 0.1%
ValueCountFrequency (%)
100 71936
16.4%
95.2 1
 
< 0.1%
95 2
 
< 0.1%
94.7 1
 
< 0.1%
94.4 3
 
< 0.1%
94.1 2
 
< 0.1%
93.8 8
 
< 0.1%
93.7 2
 
< 0.1%
93.3 15
 
< 0.1%
92.9 19
 
< 0.1%

total_bal_ex_mort
Real number (ℝ)

High correlation 

Distinct132771
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52352.807
Minimum0
Maximum3408095
Zeros233
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2025-03-05T23:39:08.779698image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6717.5
Q121387
median38741
Q366114
95-th percentile142882
Maximum3408095
Range3408095
Interquartile range (IQR)44727

Descriptive statistics

Standard deviation51067.542
Coefficient of variation (CV)0.97544993
Kurtosis83.281621
Mean52352.807
Median Absolute Deviation (MAD)20446
Skewness4.3268353
Sum2.2982411 × 1010
Variance2.6078939 × 109
MonotonicityNot monotonic
2025-03-05T23:39:08.896639image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 233
 
0.1%
30962 18
 
< 0.1%
24125 18
 
< 0.1%
26531 18
 
< 0.1%
31041 17
 
< 0.1%
31901 17
 
< 0.1%
21053 17
 
< 0.1%
20275 17
 
< 0.1%
23396 17
 
< 0.1%
23787 17
 
< 0.1%
Other values (132761) 438602
99.9%
ValueCountFrequency (%)
0 233
0.1%
1 8
 
< 0.1%
2 4
 
< 0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
3408095 1
< 0.1%
2698600 1
< 0.1%
1771306 1
< 0.1%
1310848 1
< 0.1%
1234429 1
< 0.1%
1167999 1
< 0.1%
1072366 1
< 0.1%
963229 1
< 0.1%
946682 1
< 0.1%
872627 1
< 0.1%

Interactions

2025-03-05T23:38:53.886744image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:04.627882image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:07.578433image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:10.422344image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:12.997391image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:15.810961image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:18.387970image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:21.344245image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:23.708852image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:26.482517image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:29.514818image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.458055image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.466567image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:36.817924image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:39.540749image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:42.388060image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:45.182639image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:47.966637image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:51.096305image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:54.043217image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:04.789152image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:07.731315image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:10.571346image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:13.148865image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:15.951950image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:18.541010image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:21.473244image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:23.856831image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:26.636482image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:29.674818image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.563055image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.572627image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:36.982936image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:39.700081image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:42.561317image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:45.335599image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:48.118635image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:51.249310image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:54.196719image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:04.943165image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:07.881316image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:10.715753image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:13.300801image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:16.085953image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:18.690984image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:21.605244image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:24.019039image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:26.783478image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:29.832817image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.664057image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.674609image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:37.145970image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:39.850052image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:42.716299image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:45.526862image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:48.625935image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:51.400305image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:54.358759image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:05.099131image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:08.030317image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:10.863973image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:13.441788image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:16.227960image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:18.848972image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:21.729250image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:24.167308image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:26.935918image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:29.991830image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.761055image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.773602image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:37.306016image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:40.007110image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:42.874298image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:45.675859image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:48.779946image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:51.555942image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:54.517720image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:05.251307image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:08.178320image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:11.003660image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:13.581794image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:16.365952image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:18.995008image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:21.861107image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:24.317312image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:27.085916image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:30.149822image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.872633image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
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2025-03-05T23:38:15.222320image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:17.803970image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:20.759246image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:23.185800image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:25.849688image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:28.910824image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:31.829759image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.055518image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:36.313881image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:38.927433image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:41.770408image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:44.585596image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:47.369972image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:50.479982image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:53.289552image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:56.322576image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:07.090213image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:09.977349image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:12.558604image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:15.369954image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:17.943972image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:20.905249image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:23.315782image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:25.994691image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:29.051890image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.001723image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.153562image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:36.427927image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:39.081436image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:41.924045image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:44.729640image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:47.512630image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:50.640370image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:53.431561image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:56.486578image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:07.268320image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:10.126392image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:12.700654image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:15.522968image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:18.083968image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:21.058254image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:23.435781image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:26.174689image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:29.210849image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.180724image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.250558image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:36.527979image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:39.224434image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:42.074043image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:44.874611image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:47.664719image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:50.804305image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:53.583744image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:56.646699image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:07.423364image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:10.276343image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:12.841641image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:15.665041image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:18.228027image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:21.209261image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:23.554783image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:26.331852image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:29.355820image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:32.352057image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:34.348555image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:36.630983image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:39.371438image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:42.226111image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:45.029658image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:47.812635image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:50.946305image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T23:38:53.730806image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-05T23:39:09.005640image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
acc_open_past_24mthsaddr_stateannual_incannual_inc_jointapplication_typebc_utildelinq_2yrsdtidti_jointemp_lengthfico_range_highfunded_amnthome_ownershipinq_last_12minstallmentloan_statusmort_accmths_since_last_delinqnum_accts_ever_120_pdopen_accpercent_bc_gt_75purposerevol_baltermtotal_acctotal_bal_ex_mort
acc_open_past_24mths1.0000.0160.1270.0840.028-0.184-0.0480.1560.1210.012-0.1120.0090.0290.4260.0290.0680.0960.1180.0800.480-0.1510.0140.0200.0160.4140.229
addr_state0.0161.0000.0070.0530.0530.0190.0040.0060.0440.0200.0110.0270.1210.0390.0280.0390.0090.0240.0000.0240.0240.0200.0040.0410.0300.000
annual_inc0.1270.0071.0000.6730.0000.0260.084-0.204-0.1060.0000.0820.4730.0000.1210.4450.0000.357-0.0570.0420.2710.0180.0030.4230.0020.3310.495
annual_inc_joint0.0840.0530.6731.0001.0000.0200.079-0.099-0.1410.0170.0910.4230.0690.0720.3790.0270.308-0.0600.0410.2620.0090.0330.3570.0330.3110.426
application_type0.0280.0530.0001.0001.0000.0230.0030.1361.0000.2300.0620.1350.0900.0020.1130.0780.0000.0050.0090.0240.0260.0360.0000.0920.0200.004
bc_util-0.1840.0190.0260.0200.0231.0000.0050.1890.1430.013-0.4340.0650.023-0.1150.0880.0470.0170.016-0.001-0.1080.8570.0660.3480.047-0.0850.143
delinq_2yrs-0.0480.0040.0840.0790.0030.0051.000-0.016-0.0310.003-0.2190.0040.0040.0310.0120.0070.099-0.8260.2010.0640.0030.006-0.0500.0040.1270.030
dti0.1560.006-0.204-0.0990.1360.189-0.0161.0000.5970.013-0.0180.0490.0160.0460.0630.0070.0020.013-0.0510.3180.1790.0040.2700.0120.2630.445
dti_joint0.1210.044-0.106-0.1411.0000.143-0.0310.5971.0000.018-0.0080.0630.0300.0490.0790.054-0.0070.018-0.0570.2700.1410.0510.2260.0710.2330.381
emp_length0.0120.0200.0000.0170.2300.0130.0030.0130.0181.0000.0150.0290.0960.0050.0250.0180.0070.0160.0050.0190.0170.0220.0030.0480.0370.003
fico_range_high-0.1120.0110.0820.0910.062-0.434-0.219-0.018-0.0080.0151.0000.1070.053-0.1340.0560.0680.1060.118-0.2680.047-0.3940.0400.0160.0500.0340.050
funded_amnt0.0090.0270.4730.4230.1350.0650.0040.0490.0630.0290.1071.0000.0900.0130.9680.0400.231-0.008-0.0520.1990.0510.1010.4520.4420.2210.335
home_ownership0.0290.1210.0000.0690.0900.0230.0040.0160.0300.0960.0530.0901.0000.0350.0740.0400.0160.0260.0030.0680.0270.1010.0130.1070.1060.007
inq_last_12m0.4260.0390.1210.0720.002-0.1150.0310.0460.0490.005-0.1340.0130.0351.0000.0290.0490.1280.0230.0910.198-0.1050.019-0.0550.0130.2180.156
installment0.0290.0280.4450.3790.1130.0880.0120.0630.0790.0250.0560.9680.0740.0291.0000.0360.195-0.014-0.0410.1900.0720.0960.4380.3320.1990.317
loan_status0.0680.0390.0000.0270.0780.0470.0070.0070.0540.0180.0680.0400.0400.0490.0361.0000.0060.0160.0050.0130.0430.0290.0030.0990.0400.004
mort_acc0.0960.0090.3570.3080.0000.0170.0990.002-0.0070.0070.1060.2310.0160.1280.1950.0061.000-0.0600.0750.1750.0240.0070.2630.0080.3800.226
mths_since_last_delinq0.1180.024-0.057-0.0600.0050.016-0.8260.0130.0180.0160.118-0.0080.0260.023-0.0140.016-0.0601.0000.070-0.0500.0100.0070.0250.015-0.054-0.017
num_accts_ever_120_pd0.0800.0000.0420.0410.009-0.0010.201-0.051-0.0570.005-0.268-0.0520.0030.091-0.0410.0050.0750.0701.0000.011-0.0100.007-0.1380.0100.1270.000
open_acc0.4800.0240.2710.2620.024-0.1080.0640.3180.2700.0190.0470.1990.0680.1980.1900.0130.175-0.0500.0111.000-0.0610.0270.4020.0690.7280.487
percent_bc_gt_75-0.1510.0240.0180.0090.0260.8570.0030.1790.1410.017-0.3940.0510.027-0.1050.0720.0430.0240.010-0.010-0.0611.0000.0590.3130.058-0.0480.129
purpose0.0140.0200.0030.0330.0360.0660.0060.0040.0510.0220.0400.1010.1010.0190.0960.0290.0070.0070.0070.0270.0591.0000.0000.1070.0250.011
revol_bal0.0200.0040.4230.3570.0000.348-0.0500.2700.2260.0030.0160.4520.013-0.0550.4380.0030.2630.025-0.1380.4020.3130.0001.0000.0020.3310.487
term0.0160.0410.0020.0330.0920.0470.0040.0120.0710.0480.0500.4420.1070.0130.3320.0990.0080.0150.0100.0690.0580.1070.0021.0000.0920.007
total_acc0.4140.0300.3310.3110.020-0.0850.1270.2630.2330.0370.0340.2210.1060.2180.1990.0400.380-0.0540.1270.728-0.0480.0250.3310.0921.0000.504
total_bal_ex_mort0.2290.0000.4950.4260.0040.1430.0300.4450.3810.0030.0500.3350.0070.1560.3170.0040.226-0.0170.0000.4870.1290.0110.4870.0070.5041.000

Missing values

2025-03-05T23:38:56.995764image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-05T23:38:57.872312image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-05T23:38:59.460155image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

funded_amnttermint_rateinstallmentemp_titleemp_lengthhome_ownershipannual_incloan_statuspurposezip_codeaddr_statedtidelinq_2yrsearliest_cr_linefico_range_highmths_since_last_delinqopen_accrevol_balrevol_utiltotal_accapplication_typeannual_inc_jointdti_jointinq_last_12macc_open_past_24mthsbc_utilmort_accnum_accts_ever_120_pdpercent_bc_gt_75total_bal_ex_mort
0400036 months11.99%132.84Fraud analyst8 yearsRENT45000.0Fully Paiddebt_consolidation853xxAZ0.670Jul-12744NaN711026.90%9IndividualNaNNaN5.0414.7000.01102
1720036 months11.47%237.33Client Relations Manager< 1 yearRENT85000.0Currentdebt_consolidation972xxOR10.170Mar-0466935.061396490.10%10IndividualNaNNaN2.0293.200100.033021
22000036 months16.29%706.01Registerd nurse8 yearsMORTGAGE56000.0Currentdebt_consolidation349xxFL33.710May-0566439.0211119849.10%33IndividualNaNNaN0.01278.63075.054298
31600060 months12.99%363.97Financial Advisor5 yearsMORTGAGE110000.0Currentdebt_consolidation601xxIL20.530Aug-8867435.0144070978.10%25IndividualNaNNaN2.0796.93287.545733
42800060 months15.31%670.69Pharmacist-in-Charge3 yearsMORTGAGE180000.0Currentdebt_consolidation373xxTN24.560Aug-9868437.01412821396%33IndividualNaNNaN2.0597.840100.0273107
52600036 months13.67%884.46Director, Installation & Support8 yearsMORTGAGE82800.0Fully Paiddebt_consolidation800xxCO17.480Aug-8966440.0194309183%32IndividualNaNNaN1.0988.53187.543091
62500036 months12.99%842.23equipment operator10+ yearsMORTGAGE103000.0Currentdebt_consolidation838xxID19.970Jul-0168442.016836943.40%39IndividualNaNNaN4.0967.36750.031897
7720036 months14.46%247.70Administrative Assistant< 1 yearRENT35000.0Fully Paiddebt_consolidation208xxMD24.340Jun-08699NaN131310933.50%17IndividualNaNNaN0.0546.90028.637558
81400060 months18.49%359.26Manager10+ yearsMORTGAGE62000.0Fully Paiddebt_consolidation982xxWA38.200Aug-99664NaN122645381.40%50IndividualNaNNaN0.0688.23083.3130464
91110036 months19.99%412.46Registered Nurse10+ yearsRENT55000.0Currentdebt_consolidation152xxPA28.211Nov-9467954.023477018%33IndividualNaNNaN7.02221.0000.027873
funded_amnttermint_rateinstallmentemp_titleemp_lengthhome_ownershipannual_incloan_statuspurposezip_codeaddr_statedtidelinq_2yrsearliest_cr_linefico_range_highmths_since_last_delinqopen_accrevol_balrevol_utiltotal_accapplication_typeannual_inc_jointdti_jointinq_last_12macc_open_past_24mthsbc_utilmort_accnum_accts_ever_120_pdpercent_bc_gt_75total_bal_ex_mort
4389811500060 months16.02%364.94Tech< 1 yearRENT45000.0Currentmoving956xxCA35.790Jun-0567440.08468243%15Joint App86000.019.495.0472.90050.062812
4389822500036 months23.88%979.25Leasing Consultant< 1 yearRENT62000.0Currentdebt_consolidation303xxGA25.930Oct-10664NaN171891857.50%26Joint App104000.027.067.0479.50050.074825
438983600036 months5.32%180.69NaNNaNRENT17350.0Currentdebt_consolidation530xxWI9.970Oct-00744NaN3525640.10%35IndividualNaNNaN0.0040.1000.0125784
438984800036 months7.97%250.59Pressman7 yearsMORTGAGE60000.0Currentdebt_consolidation932xxCA16.634Jul-0268912.09569121.60%22IndividualNaNNaN2.0521.62016.755875
438985800036 months9.93%257.88Gaming Service Technician2 yearsRENT45000.0Currentdebt_consolidation609xxIL12.670Nov-0168428.04245276.60%17IndividualNaNNaN1.0176.61150.010364
4389861000060 months24.85%292.64Senor System Administrator10+ yearsMORTGAGE148000.0Currenthome_improvement201xxVA4.872Nov-946697.05430372.90%9Joint App190000.03.970.0293.710100.04303
4389871000060 months17.09%249.01Sr consumer engagement specialist10+ yearsOWN44700.0Currentdebt_consolidation494xxMI24.130Aug-0066434.012550645.10%30IndividualNaNNaN1.0693.027100.033061
4389881200036 months6.72%369.00Western Regional Manager< 1 yearRENT150000.0Currentdebt_consolidation981xxWA9.231Apr-9467421.0171843228%37IndividualNaNNaN3.0432.9250.023778
438989400036 months15.05%138.76Senior Electrical Designer< 1 yearMORTGAGE93600.0Currentother980xxWA13.210Nov-0466452.08537070.70%20Joint App171600.09.657.080.0110.054727
4389902000036 months10.91%653.93NaNNaNMORTGAGE0.0Currentdebt_consolidation720xxARNaN0Sep-02759NaN11820429.80%16Joint App52000.012.091.0556.3100.08204